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Detecting Swarm Degradation: Measuring Human and Machine Performance

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Virtual, Augmented and Mixed Reality (HCII 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14027))

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Abstract

Swarms comprise robotic assets that operate via local control algorithms. As these technologies come online, understanding how humans interact with these systems becomes more important. The present work replicated a recent experiment aimed at understanding humans’ competence in identifying when and the extent to which swarms experience degradation (defined as assets breaking from consensus), as asset loss is expected in deployed swarm technologies. The present work also analyzed cluster formation in swarm simulations and explored its relationship with actual degradation. The present work replicated past findings showing people are not competent in detecting and estimating swarm degradation in flocking tasks. However, the cluster analysis showed clusters formed in simulations correlate with swarm reliability. Future work ought to expand investigations of methods to optimize cluster analysis techniques for real-time use. The implications of this work provide suggestions to interface designers on features to display to operators in human-swarm interaction.

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Acknowledgements

Distribution A. Approved for public release; distribution unlimited. AFRL-2022-0735; Cleared 17 February 2022. The views, opinions, and/or findings contained in this article are those of the author and should not be interpreted as representing the official views or policies, either expressed or implied, of the U.S Air Force, Air Force Research Laboratory, or the Department of Defense. This research was supported, in part, by the Air Force Research Laboratory (contract # FA-8650-16-D-6616).

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Authors and Affiliations

  1. Air Force Research Laboratory, Wright-Patterson AFB, OH, 45433, USA

    August Capiola, Dexter Johnson, Joseph B. Lyons & Elizabeth L. Fox

  2. General Dynamics Information Technology, Dayton, OH, 45431, USA

    Izz aldin Hamdan

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  1. August Capiola
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  2. Dexter Johnson
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  3. Izz aldin Hamdan
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  4. Joseph B. Lyons
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  5. Elizabeth L. Fox
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Correspondence to August Capiola .

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  1. U.S. Army Research Laboratory, Aberdeen Proving Ground, MD, USA

    Jessie Y. C. Chen

  2. U.S. Army Combat Capabilities Development Command Soldier Center, Orlando, FL, USA

    Gino Fragomeni

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Capiola, A., Johnson, D., Hamdan, I.a., Lyons, J.B., Fox, E.L. (2023). Detecting Swarm Degradation: Measuring Human and Machine Performance. In: Chen, J.Y.C., Fragomeni, G. (eds) Virtual, Augmented and Mixed Reality. HCII 2023. Lecture Notes in Computer Science, vol 14027. Springer, Cham. https://doi.org/10.1007/978-3-031-35634-6_23

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  • DOI: https://doi.org/10.1007/978-3-031-35634-6_23

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